Literature DB >> 30180653

Time series analysis in earthquake complex networks.

Denisse Pastén1, Zbigniew Czechowski2, Benjamín Toledo1.   

Abstract

We introduce a new method of characterizing the seismic complex systems using a procedure of transformation from complex networks into time series. The undirected complex network is constructed from seismic hypocenters data. Network nodes are marked by their connectivity. The walk on the graph following the time of succeeding seismic events generates the connectivity time series which contains, both the space and time, features of seismic processes. This procedure was applied to four seismic data sets registered in Chile. It was shown that multifractality of constructed connectivity time series changes due to the particular geophysics characteristics of the seismic zones-it decreases with the occurrence of large earthquakes-and shows the spatiotemporal organization of these seismic systems.

Year:  2018        PMID: 30180653     DOI: 10.1063/1.5023923

Source DB:  PubMed          Journal:  Chaos        ISSN: 1054-1500            Impact factor:   3.642


  2 in total

Review 1.  Complex networks and deep learning for EEG signal analysis.

Authors:  Zhongke Gao; Weidong Dang; Xinmin Wang; Xiaolin Hong; Linhua Hou; Kai Ma; Matjaž Perc
Journal:  Cogn Neurodyn       Date:  2020-08-29       Impact factor: 3.473

2.  Complex Networks and the b-Value Relationship Using the Degree Probability Distribution: The Case of Three Mega-Earthquakes in Chile in the Last Decade.

Authors:  Fernanda Andrea Martín; Denisse Pastén
Journal:  Entropy (Basel)       Date:  2022-02-26       Impact factor: 2.524

  2 in total

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